Expression Profile and Potential Functions of Circulating Long Noncoding RNAs in Acute Ischemic Stroke in the Southern Chinese Han Population
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Background: Long noncoding RNAs (lncRNAs) have been confirmed to be associated with ischemic stroke (IS); however, their involvement still needs to be extensively explored. Therefore, we aimed to study the expression profile of lncRNAs and the potential roles and mechanisms of lncRNAs in the pathogenesis of acute ischemic stroke (AIS) in the Southern Chinese Han population. Methods: In this study, lncRNA and mRNA expression profiles in AIS were analyzed using high-throughput RNA sequencing (RNA-Seq) and validated using quantitative real-time polymerase chain reaction (qRT-PCR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and network analyses were performed to predict the functions and interactions of the aberrantly expressed genes. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of lncRNAs in AIS. Results: RNA-Seq analysis showed that 428 lncRNAs and 957 mRNAs were significantly upregulated, while 791 lncRNAs and 4,263 mRNAs were downregulated in patients with AIS when compared with healthy controls. GO enrichment and KEGG pathway analyses of differentially expressed genes showed that the apoptosis, inflammatory, oxidative and calcium signaling pathways were potentially implicated in AIS pathology. The PCR results showed that the selected lncRNA-C14orf64 and lncRNA-AC136007.2 were significantly downregulated in AIS. ROC curve analysis showed that the area under the ROC curve (AUC) values of lncRNA-C14orf64 and lncRNA-AC136007.2 between AIS and healthy controls were 0.74 and 0.94, respectively. Conclusion: This study provides evidence of altered expression of lncRNAs and their potential functions in AIS. Our findings may facilitate pathological mechanistic studies of lncRNAs in AIS and provide potential diagnostic biomarkers and therapeutic targets for AIS.Keywords:
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The current study aims to investigate differences in whey protein of breastmilk of volunteered mother collected from two ethnic groups (Korean and Han) in China using data-independent acquisition (DIA) based proteomics technique. The total detected 624 proteins were principally allocated to cellular process of biological process (BP), cell and cell part of cell component (CC) and binding of molecular function (MF) according to Gene Ontology (GO) annotation; and carbohydrate metabolism of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Among the 54 differently expressed proteins, 8 were related with immunity. Enrichment data showed that intracellular of GO functions and viral myocarditis of KEGG pathways were most significantly enriched (p < 0.05). Protein-protein interaction (PPI) network suggested that 40S ribosomal protein S27a and 60S ribosomal protein L10a which interacted most with other proteins ranked the top two hub proteins by MCC (Maximal Clique Centrality) method. This study may have guiding role for development of infant formula powder for specific infants of Han or Korean groups according to responding breastmilk composition.
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This article presents data on genes associated with cleft palate (CP), retrieved through both a full-text systematic review and a mouse genome informatics (MGI) database search. In order to group CP-associated genes according to function, pathway, biological process, and cellular component, the genes were analyzed using category enrichment bioinformatics tools, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). This approach provides invaluable opportunities for the identification of candidate pathways and genes in CP research.
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Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide and mostly occurs in viral hepatitis endemic areas such as China. Knowledge of HCC-related genes may lead to an early detection of HCC and develop molecularly targeted therapeutics, reducing mortality and improving a patient’s prognosis significantly. Therefore, it is valuable and important for us to identify common characters of HCC related genes. In this study, we proposed a computational method to predict HCC related genes based on Gene Ontology terms and KEGG terms using Random Forest (RF), in which features were optimized by maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). 224 HCC gene candidates were compiled from some databases, while 11,200non-HCC gene candidates were randomly selected from Ensemble database. 10 candidate datasets were constructed by dividing non-HCC gene candidates into 10 groups. Each gene in datasets was encoded by 13,126 features including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 615 GO terms and 11 KEGG pathways was discovered. Through analysis, we found these features were closely related to HCC, which means our method is effective for discovering HCC related genes, and it is hopeful that it can also be used to predict and analyze genes for other types of cancer. Keywords: Gene ontology, hepatocellular carcinoma (HCC), incremental feature selection (IFS), KEGG, maximum relevance minimum redundancy (mRMR), random forest (RF).
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A Pomegranate Peel Extract (PGE) has been proposed as a natural antifungal substance with a wide range of activity against plant diseases. Previous studies showed that the extract has a direct antimicrobial activity and can elicit resistance responses in plant host tissues. In the present study, the transcriptomic response of orange fruit toward PGE treatments was evaluated. RNA-seq analyses, conducted on wounded fruits 0, 6, and 24 h after PGE applications, showed a significantly different transcriptome in treated oranges as compared to control samples. The majority (273) of the deferentially expressed genes (DEGs) were highly up-regulated compared to only 8 genes that were down-regulated. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis showed the involvement of 1233 gene ontology (GO) terms and 35 KEGG metabolic pathways. Among these, important defense pathways were induced and antibiotic biosynthesis was the most enriched one. These findings may explain the underlying preventive and curative activity of PGE against plant diseases.
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Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.
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Rhinitis is a disorder of the nasal mucosa with inflammatory responses, which negatively impairs work performance and life quality. Hedysarum Multijugum Maxim (HMM) has been widely used for preventing and treating rhinitis. However, the pharmacologic effects of HMM and its mechanisms against rhinitis have not been fully studied. The bioactive compounds of HMM were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). The targets genes of HMM for treating rhinitis were identified using PharmMapper and GeneCards database integration. Protein-protein interaction analysis (PPI) was carried out using the GeneMANIA database. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were carried out using clusterProfiler. The drug-target-disease-GO-KEGG networks were built via Cytoscape. A total of 14 bioactive ingredients were screened and 25 target genes of HMM acting on rhinitis were discovered. PPI analysis demonstrated that these genes and their related genes had physical interactions or displayed co-expression characteristics. GO, KEGG and network analyses demonstrated that these targets were closely related to epithelial cell proliferation, regulation of inflammatory response, endocrine resistance, MAPK, VEGF, and TNF signaling pathways. In short, HMM displays multi-compounds, multi-targets to exert systematic pharmacologic actions on rhinitis.
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Introduction: Time-dependent effects of laser radiation have been investigated by researchers. An understanding of the molecular mechanism of the time course effect of the laser needs molecular assessment and function evaluation of the related genes. In the present study, the importance of repetition of treatment after 4 weeks and gene expression alteration after 7 days of laser radiation versus one day on the human skin was evaluated via protein-protein interaction (PPI) network analysis and gene ontology enrichment. Methods: The differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus (GEO) and assessed via PPI network analysis. The critical DEGs were enriched via gene ontology. The related biological processes and biochemical pathways were retrieved from "GO-Biological process" and "Kyoto Encyclopedia of Genes and Genomes" (KEGG) respectively. Results: The repetition of laser therapy after 4 weeks of the first treatment did not have a significant effect on treatment efficacy. Sixty-three significant DEGs and six classes of biological terms discriminated the samples seven days after the treatment from individuals one day after the treatment. The studied DEGs were organized into two clusters with certain functions. Conclusion: Based on the findings after laser therapy, several days are required to complete the critical processes such as DNA biosynthesis and skin cornification.
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Ablative case
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Protein-protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein-protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein-protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.
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Abstract Objective To investigate the role of miR-449a-5p overexpressed in rats with spinal cord injury (SCI), and to predict the potential targets of miR-449a-5p by bioinformatic analysis and explore the underlying mechanism of the onset and development of axonal reconstruction in SCI. Methods Forty-eight SD female rats were randomly divided into Sham group, SCI group, agonist group (SCI + miR-449a-5p agomir), and agonist control group (SCI + agomir negative control).Basso-Beattie-Bresnahan (BBB) scores were performed after the successful establishment of SCI rat model. The targets of miR-449a-5p was searched in TargetSccan, miRDB, and miRWalk databases, and relevant literatures were retrieved in PubMed and CNKI using ‘miR-449a’ and ‘axonal regeneration’ as keywords, and genes not reported previously were subjected to Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, and protein-protein interaction (PPI) network, and the hub genes were screened by Cytoscape. Results The BBB score results showed that the rats in SCI + miR-449a-5p agomir group exhibited gradual recovery after SCI surgery, with significant improvement compared to the SCI group. A total of 76 target genes of miR-449a-5p were obtained, and the hub genes were CNTN2, ANK3 and CNTNAP1. Conclusions The present study suggested that miR-449a-5p may participate in a variety of BP through target genes, and hub genes CNTN2, ANK3, and CNTNAP1 may provide help for the exploration of the mechanism of axonal reconstruction in SCI.
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One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.
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Retinoblastoma
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